In practice, experimental data contains not only natural variability but also noise. It is known that pure sparse regression based model discovery cannot handle high noise levels (>5 \(\%\) ) - for such cases, deep learning model discovery frameworks excel see [1]}, [2]} . Here, we extend DeepMod [1]} to handle multiple experiments (see note REF ). The framework combines a function approximator of \(u\) , typically a deep neural network which is trained with the following loss,
\(\mathcal {L} = \sum _{i=1}^{q} \Big ( \underbrace{\frac{1}{n} ||u_i-\hat{u}_i ||_{2}^{2}}_{\mathcal {L}_{\textit {MSE}}} + \underbrace{\frac{1}{n} ||\partial _t \hat{u}_i - \Theta _i (\hat{\xi }_i \cdot M_i) ||_{2}^{2}}_{\mathcal {L}_{\textit {reg}}} \Big )\) 
